768GB of cheap Intel Optane DIMM memory sticks used to run 1-trillion-parameter LLM on a system with a single GPU — local Kimi K2.5 install achieved roughly 4 tokens per second
A Redditor has caused a stir by coaxing a workstation build using Optane PMem DIMMs as RAM to run a 1-trillion parameter LLM.
The rapid advancement in LLM capabilities and the increasing demand for local inference drives innovation in memory and processing configurations, making novel solutions like this timely.
This development suggests new avenues for democratizing access to large language models, reducing the compute barrier for local AI development and deployment.
The perceived minimum hardware requirements for running very large LLMs are being significantly challenged, potentially broadening the base of users capable of local AI inference.
- · AI enthusiasts/developers
- · Intel (Optane users)
- · Open-source AI community
- · Edge computing
- · High-end GPU manufacturers (sole reliance)
- · Cloud AI service providers (some use cases)
- · Proprietary memory solutions
This experiment demonstrates that creative hardware configurations can substantially lower the cost and complexity of deploying large AI models locally.
Increased local LLM capability could accelerate privacy-preserving AI applications and reduce reliance on centralized cloud services for many tasks.
A future where personal devices run multi-trillion parameter models could usher in a new era of personalized, offline AI assistants and agents.
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Read at Tom's Hardware